Enhanced genetic path planning for autonomous flight

Path planning, the task of finding an obstacle-avoiding, shortest-length route from source to destination is an interesting theoretical problem with numerous applications. We present an improved genetic algorithm for path planning in a continuous, largely unconstrained real-world environment. We introduce a new domain-specific crossover operator based on path intersections. We also implement a new path correction operator that eliminates obstacle collisions from a path, leading to a dramatic search improvement despite the conceptual simplicity of the correction. Finally, in place of a standard binary measure of obstacle collisions, we present a new optimization objective measuring the degree to which a path intersects obstacles. Due to these improvements, individually and in combination, our algorithm is able to solve scenarios that are considerably more complex and exist in a more general environment than those that appear in the literature. We demonstrate the utility of our system through testing onboard an autonomous micro aerial vehicle. Further, our approach demonstrates the utility of domain-specific genetic operators for path planning. We hypothesize that such operators may be beneficial in other domains.